Get up and running with the basics of geographic information systems
(GIS), geospatial analysis, and machine learning on spatial data in
Python. This book starts with an introduction to geodata and covers
topics such as GIS and common tools, standard formats of geographical
data, and an overview of Python tools for geodata. Specifics and
difficulties one may encounter when using geographical data are
discussed: from coordinate systems and map projections to different
geodata formats and types such as points, lines, polygons, and rasters.
Analytics operations typically applied to geodata are explained such as
clipping, intersecting, buffering, merging, dissolving, and erasing,
with implementations in Python. Use cases and examples are included. The
book also focuses on applying more advanced machine learning approaches
to geographical data and presents interpolation, classification,
regression, and clustering via examples and use cases. This book is your
go-to resource for machine learning on geodata. It presents the basics
of working with spatial data and advanced applications. Examples are
presented using code (accessible at
github.com/Apress/machine-learning-geographic-data-python) and
facilitate learning by application.**
What You Will Learn**
- Understand the fundamental concepts of working with geodata
- Work with multiple geographical data types and file formats in Python
- Create maps in Python
- Apply machine learning on geographical data
Who This Book Is For
Readers with a basic understanding of machine learning who wish to
extend their skill set to analysis of and machine learning on spatial
data while remaining in a common data science Python environment
